Industry studies suggest nearly 87% of enterprises missed their revenue targets even amid record AI investments. In this candid conversation, Mike Meyer, Chief Information Officer, Clari / Salesloft explains why AI underperforms without a predictive revenue system, what it takes to move from pilots to production, and how leaders can turn AI from informational to operational.
Industry studies suggest that nearly 87% of enterprises missed their revenue targets. What’s the biggest reason behind this gap?
The gap exists because most AI is deployed without a predictive revenue system underneath it. This system is a way of running revenue where data, workflows, and AI work together to continuously anticipate risk, guide the right actions, and help teams make smarter decisions across the entire go-to-market process.
The survey shows that 87% of enterprises missed revenue targets despite record AI investment because AI is operating on fragmented, ungoverned revenue data. AI can’t predict or prescribe outcomes when it lacks revenue context, or the connection between actions, signals, and results across the revenue lifecycle. Without that context, AI produces insights that aren’t actionable. Enterprises haven’t failed at AI innovation; they just haven’t yet modernized revenue into a system that AI can reliably power.
“Enterprises haven’t failed at AI innovation—they just haven’t modernized revenue into a system that AI can reliably power.” — Mike Meyer, Chief Information Officer, Clari / Salesloft
Are organizations treating AI as a technology experiment rather than a business transformation—and how does that impact ROI?
In 2025, we saw a surge in AI experimentation, with many companies using the technology to test new ways of working. But Clari’s data shows a clear pattern: AI is still treated as a siloed initiative, led primarily by IT, while revenue leaders and RevOps remain on the sidelines. That disconnect has the potential to create shadow AI, where teams without the authority to deploy software end up doing so anyway. The result is fragmentation instead of scale. AI insights surface, but they rarely translate into execution, and ROI stalls because insight alone, without coordinated behavior change across revenue teams, does not drive revenue.
In 2026, that changes. We’re entering the era of AI operationalization, where enterprises move beyond experimentation and embed AI directly into core revenue workflows like forecasting, pipeline management, and deal execution. To do this effectively, companies need a predictive revenue system powered by revenue context, or the shared data, cadences, and workflows that unify go-to-market teams and turn AI from passive insight into proactive action.
Why do so many AI projects struggle to move from pilot to production?
AI pilots struggle to scale because enterprise revenue complexity overwhelms them. The survey shows more than half of enterprises experience conflicting pipeline signals from disconnected systems. Pilots can succeed in isolation, but production demands a system that continuously captures structured and unstructured revenue signals and governs them consistently. Without revenue context, trust breaks down. Leaders won’t operationalize AI they don’t trust, especially for forecasting and revenue decisions.
How critical is data readiness and governance to AI’s commercial success?
It’s the determining factor. Nearly half of enterprises say their revenue data isn’t AI-ready, and 42% lack formal governance frameworks. AI without governance cannot deliver predictability. A predictive revenue system turns governance into a growth strategy by ensuring AI insights are accurate, explainable, and actionable across sales, RevOps, and leadership.
Do enterprises underestimate the cost and complexity of operationalizing AI at scale?
Yes, because they often underestimate what it takes to run revenue as a system. AI at scale requires unified data models, continuous signal capture, security controls, and cross-functional accountability. CRM alone can’t provide this. A predictive revenue system is infrastructure, not a feature. Enterprises often fund AI initiatives without investing equally in the revenue architecture required to support them.
What role does leadership and change management play in turning AI insights into revenue?
Leadership determines whether AI stays informational or becomes operational. While CIO–CRO alignment is increasing, accountability and trust remain major barriers. A predictive revenue system succeeds when leaders embed AI into revenue cadences, reviews, and decision-making as the system of record for how revenue is run. Effective change management ensures revenue context becomes the shared truth across teams.
How can CIOs and business leaders better align AI use cases with revenue outcomes?
They can do this by anchoring AI to revenue outcomes, not experiments. CIOs play a significant role in revenue tool selection in most enterprises, positioning them to enforce unified data models and governance. When AI is aligned to forecasting accuracy, pipeline health, and execution discipline, it becomes part of a predictive revenue system rather than a standalone initiative.
Is the AI skills gap limiting enterprise ability to monetize AI?
The bigger gap is operational ownership, not skills. IT teams lead enablement, but RevOps involvement is limited. Without RevOps shaping how AI fits into revenue workflows, AI insights don’t translate into action. We can lower the skills barrier by increasing communication, collaboration, and interoperability across sales, marketing, finance, and revenue teams — embedding intelligence directly into execution.
How should enterprises measure AI success beyond model accuracy?
AI success should be measured by predictability, trust, and decision quality. Clari + Salesloft’s research shows that weak governance leads to constant forecast recalibration, which erodes confidence and slows decision-making. Under a predictive revenue system, success metrics are shifted from accuracy alone to whether leaders can consistently and confidently act on AI-driven insights, quarter after quarter.
Just as important, AI initiatives must be directly tied to the business metrics that matter most to the CFO: revenue predictability, pipeline conversion, forecast accuracy, and more. If organizations cannot show a clear correlation between AI-powered workflows and movement in those numbers, AI stays in the experimentation phase. AI success is proven when it changes how teams operate at scale, and materially improves the financial metrics leaders are accountable for.
What practical steps would you recommend to reverse AI underperformance?
Start by treating revenue as a system:
- Standardize and govern revenue data.
- Align CIO, CRO, and RevOps ownership.
- Embed AI directly into revenue workflows—forecasting, pipeline hygiene, deal execution.
- Build revenue context first, then enable AI to operate within a predictive revenue system designed to guide decisions, not just analyze them.